Federated learning (FL) is an emerging distributed and privacy-preserving machine learning
framework. However, the performance of traditional FL methods is seriously impaired by the …

Federated Learning (FL) has recently made significant progress as a new machine learning
paradigm for privacy protection. Due to the high communication cost of traditional FL, one …

Federated learning (FL) is a well established method for performing machine learning tasks
over massively distributed data. However in settings where data is distributed in a non-iid …

Federated Learning (FL) has shown great potential as a privacy-preserving solution to
learning from decentralized data that are only accessible to end devices (ie, clients). The …

Federated learning (FL) is a popular distributed learning framework that can reduce privacy
risks by not explicitly sharing private data. In this work, we explicitly uncover external …

Federated learning (FL) is an important technique for learning models from decentralized
data in a privacy-preserving way. Existing FL methods usually uniformly sample clients for …

To mitigate the privacy leakages and communication burdens of Federated Learning (FL),
decentralized FL (DFL) discards the central server and each client only communicates with …

Federated learning (FL) has emerged as an effective solution to decentralized and privacy-
preserving machine learning for mobile clients. While traditional FL has demonstrated its …

Federated Learning (FL) proposed in recent years has received significant attention from
researchers in that it can enable multiple clients to cooperatively train global models without …

Federated learning is a distributed, privacy-aware learning scenario which trains a single
model on data belonging to several clients. Each client trains a local model on its data and …